502 lines
129 KiB
Plaintext
502 lines
129 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5be15e75-0bdf-46bd-a4a8-a057ef156866",
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"metadata": {},
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"source": [
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"# Geometry visualization\n",
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"\n",
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"We have already been extensively using the `plot()` method on various geometric classes, including `Model`, `Geometry`, `Universe`, `Cell`, and `Region`. This method automates a series a steps as follows:\n",
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"\n",
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"- For all classes other than `Model`, it builds a single-purpose `Model` instance for plotting and calls `Model.plot(...)`\n",
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"- Creates an instance of a `Plot` object corresponding to the plot view\n",
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"- Creates a temporary directory and exports the model (with plot information) to XML\n",
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"- Runs OpenMC in plotting mode, which produces a .png file\n",
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"- Loads the image data from the .png file using matplotlib.image\n",
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"- Displays the image using matplotlib.imshow\n",
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"\n",
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"We can also manually run individual pieces of this workflow ourself to have more control over plotting (e.g., if we want to produce a series of plots). Let's walk through some of the pieces."
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]
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},
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{
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"cell_type": "markdown",
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"id": "dddd04fd-924d-432c-8fac-a2db91f06f63",
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"metadata": {},
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"source": [
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"## The `Plot` class\n",
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"\n",
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"The `Plot` class allows us to pass information to OpenMC on a slice (or voxel) plot that we want to view. Most of the attributes on this class are the same as what appears on the `Model.plot` method. Let's see how we can manually create a plot using this class. First, we will create a simple model with two half-spherical shells."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "65266181-5e0a-458c-9d30-3b1d904a9619",
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"metadata": {},
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"outputs": [],
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"source": [
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"import openmc\n",
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"from IPython.display import Image\n",
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"import matplotlib.image as mpimg\n",
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"from matplotlib import pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "209d9598-1375-4302-a17c-1d4124d81cc1",
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"metadata": {},
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"outputs": [],
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"source": [
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"mat = openmc.Material()\n",
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"mat.add_nuclide('U235', 1.0)\n",
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"mat.set_density('g/cm3', 1.0)\n",
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"\n",
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"p = openmc.XPlane()\n",
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"sph1 = openmc.Sphere(r=5.0)\n",
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"sph2 = openmc.Sphere(r=10.0)\n",
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"sph3 = openmc.Sphere(r=13.0)\n",
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"sph4 = openmc.Sphere(r=15.0)\n",
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"sph5 = openmc.Sphere(r=50.0)\n",
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"\n",
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"cell1 = openmc.Cell(fill=mat, region=+sph1 & -sph2 & -p)\n",
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"cell2 = openmc.Cell(fill=mat, region=+sph3 & -sph4 & -p)\n",
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"empty = openmc.Cell(region=-sph5 & ~cell1.region & ~cell2.region)\n",
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"\n",
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"model = openmc.Model()\n",
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"model.geometry = openmc.Geometry([cell1, cell2, empty])\n",
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"model.settings.particles = 1000\n",
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"model.settings.batches = 10\n",
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"model.settings.inactive = 5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "5c50938f-8e28-40ce-9af8-d07d5375b6e9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
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]
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},
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.plot(color_by='material')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "6b9a85fc-c51c-4657-92aa-841308389d3e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plot = openmc.Plot()\n",
|
|
"plot.width = (35., 35.)\n",
|
|
"plot.origin = (0., 0., 0.)\n",
|
|
"plot.pixels = (400, 400)\n",
|
|
"plot.color_by = 'cell'\n",
|
|
"plot.filename = 'xy_slice.png'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "d8f514f9-696e-4e41-97bf-632bc994c1be",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" %%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%\n",
|
|
" ################# %%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%\n",
|
|
" ############ %%%%%%%%%%%%%%%\n",
|
|
" ######## %%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%\n",
|
|
"\n",
|
|
" | The OpenMC Monte Carlo Code\n",
|
|
" Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
|
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" License | https://docs.openmc.org/en/latest/license.html\n",
|
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" Version | 0.15.3\n",
|
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" Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
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" Date/Time | 2025-11-27 09:59:20\n",
|
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" OpenMP Threads | 2\n",
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"\n",
|
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" Reading model XML file 'model.xml' ...\n",
|
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" Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
|
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" Preparing distributed cell instances...\n",
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"\n",
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" =======================> PLOTTING SUMMARY <========================\n",
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"\n",
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" Processing plot 22: xy_slice.png...\n",
|
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"Plot ID: 22\n",
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"Plot file: xy_slice.png\n",
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"Universe depth: -1\n",
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"Plot Type: Slice\n",
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"Origin: 0 0 0\n",
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"Width: 35 35\n",
|
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"Coloring: Cells\n",
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"Basis: XY\n",
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"Pixels: 400 400\n",
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"\n"
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]
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}
|
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],
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"source": [
|
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"model.plots = [plot]\n",
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"model.plot_geometry()"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "44b74fd7-31ed-4724-a2c3-6354757679e7",
|
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"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"Image('xy_slice.png')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "f77c0872-8158-467b-8b1f-757c5dc42610",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.image.AxesImage at 0x7f4a6d709d30>"
|
|
]
|
|
},
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"img = mpimg.imread('xy_slice.png')\n",
|
|
"plt.imshow(img) # it will show the extend of the plot"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"id": "b0b6de07-230b-4f45-a9a8-17e1b9424e6a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.plot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fc39d981-5351-470f-a4e3-91e96eb8b0fb",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 3D visualization: Voxel plots\n",
|
|
"\n",
|
|
"There are a few ways to perform 3D visualization in OpenMC. The oldest and most rudimentary method is a voxel plot, which is essentially a 3D generalization of the slice plot where the geometry is rasterized over a 3D grid of plots. To generate a voxel plot, we can use the same `Plot` class but just set the type equal to 'voxel' (otherwise it defaults to 'slice'). Additionally, when we give the width/pixels, they need to specify three coordinates."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"id": "cacdb476-bcdb-44be-abc9-57a5c7f67f35",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" %%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%\n",
|
|
" ################# %%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%\n",
|
|
" ############ %%%%%%%%%%%%%%%\n",
|
|
" ######## %%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%\n",
|
|
"\n",
|
|
" | The OpenMC Monte Carlo Code\n",
|
|
" Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
|
|
" License | https://docs.openmc.org/en/latest/license.html\n",
|
|
" Version | 0.15.3\n",
|
|
" Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
|
|
" Date/Time | 2025-11-27 09:59:20\n",
|
|
" OpenMP Threads | 2\n",
|
|
"\n",
|
|
" Reading model XML file 'model.xml' ...\n",
|
|
" Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
|
|
" Preparing distributed cell instances...\n",
|
|
"\n",
|
|
" =======================> PLOTTING SUMMARY <========================\n",
|
|
"\n",
|
|
" Processing plot 24: voxel_plot.h5...\n",
|
|
"Plot ID: 24\n",
|
|
"Plot file: voxel_plot.h5\n",
|
|
"Universe depth: -1\n",
|
|
"Plot Type: Voxel\n",
|
|
"Origin: 0 0 0\n",
|
|
"Width: 21.42 21.42 20\n",
|
|
"Coloring: Cells\n",
|
|
"Voxels: 100 100 50\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"plot = openmc.Plot()\n",
|
|
"plot.type = 'voxel'\n",
|
|
"plot.width = (21.42, 21.42, 20.0)\n",
|
|
"plot.origin = (0., 0., 0.)\n",
|
|
"plot.pixels = (100, 100, 50)\n",
|
|
"plot.color_by = 'cell'\n",
|
|
"plot.filename = 'voxel_plot'\n",
|
|
"\n",
|
|
"model.plots = [plot]\n",
|
|
"model.plot_geometry()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "64ebecba-bb92-4287-a488-7ab5eee92a70",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'voxel_plot.vti'"
|
|
]
|
|
},
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"openmc.voxel_to_vtk('voxel_plot.h5', 'voxel_plot.vti')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e8cca584-4db8-4a92-a7da-c0716d527343",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 3D plots: Raytrace plots\n",
|
|
"\n",
|
|
"There are also two plot classes that allow us to produce raytraced 3D plots directly using OpenMC's geometry engine: `WireframeRayTracePlot` and `SolidRayTracePlot`. These plots operate similar to the other classes in that you create an instance of them, attach it to the `plots` attribute of a `Model`, and then run in geometry plotting mode. Like slice plots, they will produce .png files that can be viewed."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"id": "5c624a13-c55b-40c1-a513-6cf053479862",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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bEWTfgRUMjXuQN+KmbEaQfact335MWJZlltDjhwY/iEBk4l5fj9HSuDqGnww4TdwBChJ35rPCA82JO5NZdoAexJ3zTNwQVvG4q08/Jm6IrHLcA9bHzaaS6x9l8WEY+qkc9zjuTS95GbtdQUzi3l3Jpt8V3rWwDPvsJO6E4CcAaGtC3F3GxVx/QY2i0NzouLuMi1n2BbU8QnCWZUab9YNL1+f10xhEUz/uoboza9br+rytHjzUKwXZFY+7H5yHuZhmrxS0VTzujLFamscsuFvW54p1424RoDmH9BDtpqtF4z73oioZwSaHtOSRgSnmxH3la7j2sHblla19ZGCwCXEffw1HuJdE2IbeFqmz5RRSqL8sE+E6jLANt1E3mIvPssJdEAaoH/eYxidszA3m4rMEuQuG5YcG9hP3Ccpfn4Wnb3klC3GnsXv7rvS98L3hwU2C3laJ+wq9iONK3yUPmpgW95G1jdmL2vebmMf8opHjttGei+bEPchZOzGvs47A4F0+/XS173wwwCrLMu8W/Lj94F0+vTgT5N7/wihNLuvGfbCPXZie+96uLL6XPzjQ1UJxjxaLRcbAc32PdnDaju1+CGCAmXH3nuoiTs/v0e7Hc7klcMi0uDtN7xbp14m+xzlDmo/trR4KNiy0LBPQlH7Nisu5+b1YCnfeJwzpXLdW3GOWYp3lqaN9jxC4hp2VbEZaKO4RPq/y/nSrXe0n5veYt+SjHmWXeMaYHPe51+2Uz31zqO/T/2ZWq78w5dVnsJlxX2rFeVvMrern6Pw+5fgoO6kttCxzmz0G/mbB35W9PfX968Zc/5rJWe57F/Oso7y14h7ZsHjFac3Pz8/OcI/v+/Uo3x/h5UH2POzH/8ZNgqPmxz3jRNbcytftob6P0arsrbYHTpgc9wWX3d3M3u1fohlw9C522VIMQcyf3Af7+APvxGeftSXR7jHPSzS/bVvwxfdH1pWdCJaL+4s41+HILYmz1y++Jr53388N3bJOQCHiPn4Wizz9zd6E+bYT36nv55ZTDmXdcg0jzY/7+NM97AUWdsOm2Eh8876fmLs7Tes+KkMr8+POi2HDe4qfEn5L/P73YL86mk6LMKQQJe5LrcxsPPWwXuQK00vi7wdw/8fkN+wv++Opz2Xd9M1g/8zegNvtdvv5+Rmc2pdnHHnh7dlZIfjocUyeB/b78Xzkfv+j7fwnz090dINhohBxn2v8rWXbyO1Jehf5WPnbvl5/jfXH9X1IZ+m4R07bgG2Ldlc74f2X+2/fVmme/8nGpy2niHxCkk6guA8+s+embXtnC2R3io+H9OVIvr8rO8CVc1vxOSdK3GflbMqVs3NnXdVNOIasKcqnZaaIfNmPHCrHPBEwUqy4x/x44kS9tyry7a2MnT+B+UGNtgLFfdaZHfaiCv49WUBkgeI+xdyPRnz9b8ZsnvtHWGEnD+ILF/dZb6sOfsYTv24TYUs4Sp2ZJVbcp1wGwa89izMrcA+guVhxnytsQAdc9mH3HTgnYtzXCc2hPe13WMyMnZjHmShc3NdZmTn0pIb3Bbk3cEW4uLPB8F6SiNND0LjP+vhK5Ok1/hbyTLKZK2Lcl7okDsW6d9/dOaCMiHG/C/7Z81nP2G8jl7qn9nZ9bDf4c1HQuAf/xdHpUmwke4g4nQSN+1KOlrrr4ozbBtQQOu6zQjPyec9NbZ36boRswjBOBHHjHv+z53P121TD+zC/3QbcHrgubtzvVhjerzC8R6PLBBE67rMukvHPe67R/Rbfs9zbgN+Ejvss97QNC9yVe0mPvhs8TzO2E0eCuA+eIu8XZ6JLtNP8bnjvzYI7XUWP+9yzfGTgrjxX876LywmiTCjR4343foocfKFef64eW2t4h7wSxH3utwLkCpzhfZaj04Axn94SxP1uSmQHfxHjxSfqsbW57m0FiD6t5Ij7xNN92FM3eaK2fVeZnRSZgHLE/W7wFPn8dIkG2Obze6J9z8LNgAHSxH3wxfD8dCOfuklJG/Zdg75SamJKE/e7iVPkgKdu2Ii287vhfQz3CRrKFPfpK++5Gtdqm+Vmw4kcKzhjZIr7eM9lHHZBNv84Y5MHzHVjG0OmiSxf3Od+5UvvZ28eiyZ9z/iDS0wb9wO3CtpKFve5Z3/G4f3WtO88aDHBJYv73dyvfEk3vN+e+n5x4w3vkEW+uI9cInjvbN4Fip+fn4sbb1B9ODe2W5NhpHxxv82uzJhn73T/uH5zynhja0uISSFl3O/mfuVL12fv2o4rfc/7gwusJmvc537ly5jG9Xv8631f1umx3ZoMg2WN+93EEbL31Tjm8U+/xbrm8K7CJJI47hG+8iV1406/xWpx5ih3BcZLHPe7iV/5MqBxw/ZO37/qFGjdp5PccZ9+VXRt3OD3FY4u0Uw/+CNdSbB8M0XuuN+NmR9/e5be1+2wT/Sfu1GtMLyrMxmlj/uYq+7rs2Qf3p+fbv++rLA407Xsbhv0kz7ud3P70rtxI/fu6BJN7b5fj698M0uFuA/ry8ZT9NuG8Wl4XqLZs0e1+w5JVYj7bUgBvz5FscYdWoUvtu93vcd2Qz1dFYn73dzh/dbzHjOrm/tH+GJ9V16yqxP3AXHZebU334bpX5S289iW6XuTsrs9MFeduN9mR/B5G3oELsibxnt+dknd9zFRln56KxX3u+nf59UjcBFCsPON1tR9b9Vc7Wa6anEP8n1enQIXoZh7Ep+078OKLP0MUC3udxGy0jxwoXLwNfG5+n7fi1ZHWLuJoGDc43yfV6fvmWr+mKdtJz5L3+8tlmOKKRj325C/ZLRf1eH9YSPxF781foDmU/bXBzTXM0bNuN/FGd7bbknMUL4k/rGRp7+SrLe2SzEQTeWT+16TTjt4qAttt6TrfrXyHPff/peJOmXd2E4clSf3OFdR29E1zn5teB/kg4zwBnYWUTnud/1ScuiRm3ct2irHRz//7/bfqs5ahe/63ql7BqHUPx37LWKcuJgbbkzGlGx8aHLMU3f98p+vD57xJSOv+pN716WAow/bdmNSDO/Pngf5h65T/PuiECxilVFi1htov/2rW4spssAk+Nun41s9cpzfOC3wYpHLP7M3YJw4ff/5+Wn1tl72ZDxv/D3HVz5U83yrSH1Y4LrcaTik0yh3Oq9NtifFxyJP2LM632/qP8TYTkxrnXM9rrEreW3V9xVexCApf7Hz4C/yGhHKWudctOH91mKTqg7vKRjbCav+p2We9fvkzOnHvL5JEX4zaE2qTWRrxf0W8i9ptOo7I1mQIbjl4n4L+Zc0mmyS4R14WDHutw6j7vUHvPh7+RZnRjK2E9+icb8LNbzfLn87ro6MIdmksG7cm4+6rS74ixtmeA/CPYC51o37LfBf0ji9YRZnepNsslg67rfA37R+se/0sL/s7gFMt3rcb31+p6nJ41x5i9XwDosT978CDu+3s2+xWpzpwdhOLuJ+u4X/M0n6Pp1ek464/9Wwhj0qcLrvXHeo7G4DBCHu/wr+Z5LOLcEb3i8Sa5IS9/9o1fdOOTi6BG9x5qKjZXcnIA5xf9UwiJ2qqu/AV+L+QZMgdp3gDi3R6Ps5xnZSE/fPWgWxX1IPLdHo+1HKTnbi/qsUf0Zj/wiv7/spNQWI+5YUf0Zj/wiv73ucKLubAQGJ+xdZ/ozGzhFe37fJNGU4lXe5+EeoR/4N60e4N57O39T+6FzZ3Q+IyeS+y8WBd+TFv2eVxvz+TtkpRtz3uh7EkTH9ukqj7880mnqc08dcWdCYUpDtVRrrM7cLr4tbApGZ3I9J9zfwtldpzO/KTlXifljGv4G3sUqzct8FmsKc3CedXtCYG5TfVmlWW5+5/vGndY4VSTlHzzsXiAgZ/Zj4PZ+hrOFimpWdFJyml+Tt+20z8dO3rZ/raRZ3UnCaXpW677dPiY+zbW012S9lJwtnagOn+x7n4L8kvt4STZOjHeolg21O1jZO9D3ggPwx8aG28IRWe6Hs5OJ8baZG32//TXz2Eb5hkcWdXJyvLZ1IYcy+3z598j3gRm5oe2CVnXScsu0dzUrwcLxUPvKm3jW/XwZ/geAjZ20Xh/oSdnh/liXxzUOs7CTlxO2lXt/vnisfZ4P7vTcg7iTlxO3oUHES9f0WaZDvetyUnbycu93tr0+uvt/NGuQHfIxH2UnN6TvCob4nfUXGVH7YRzPzvhBw5wweZGffMw7vL3qs2Az+uL2yU4CTeJydhSrQ94eNvxDS8J+0pezU4DwebU+7K/X9bv8fA5n+ZciVDjsrcypPsGeEr9f3h8i/+yrulOFUnuZrvgv3/Vmc1is7lTibZ9L3d7PW3JWdYpzQk31dolmw7y821ut9Lxj8xjkdwnbB9f2j/W/SfuXYUo+4R7E9wut7J2Z2qnJmx7IRcX1vTtkpzMkdzsYIr+8NKTu1Ob+D+i3x+n6dY8gKxD20jxnSpisM7CzCiR7dxxFe389RdtbhXM/hveaDvyixAGVnKU73NIzwVyg7q3HGJ/OeeH3f5viwJnFP6SXx+vUbAzvLcuon9px4S/DvlJ2VOfvTe0+819RxAHEv4pEzI7yBHW7iXokRfs29ho/EvZo4f9hoMAM7PHM9lPVc+dqvsoEd3ol7cbUTL+vwG3FfQsnEW4eBDS6PhZRJvIEdvhL35eRNvE95wn7ivq5ErTSqw1Hivrrgg7yswznizl+hKp/opwqISdx5NbHymg6tiDu/evll106nSqifGKAMcWeXVt9qsOy3I8Bg4s4Z743eyfkGY4g7QEH/m70BALQn7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQeIOUJC4AxQk7gAFiTtAQf8HeJpvtAR6wEMAAAAASUVORK5CYII=",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"plot = openmc.WireframeRayTracePlot()\n",
|
|
"plot.look_at = (0., 0., 0.)\n",
|
|
"plot.camera_position = (15., -25., 0.)\n",
|
|
"plot.pixels = (500, 500)\n",
|
|
"plot.filename = 'wireframe_plot'\n",
|
|
"plot.color_by = 'cell'\n",
|
|
"plot.wireframe_domains = [cell1, cell2]\n",
|
|
"plot.mask_components = [empty]\n",
|
|
"\n",
|
|
"model.plots = [plot]\n",
|
|
"model.plot_geometry(output=False)\n",
|
|
"Image('wireframe_plot.png')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"id": "002ca76c-ffbd-4e98-bfb0-a68d99f811c2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"plot = openmc.SolidRayTracePlot()\n",
|
|
"plot.look_at = (0., 0., 0.)\n",
|
|
"plot.camera_position = (15., -25., 10.)\n",
|
|
"plot.pixels = (500, 500)\n",
|
|
"plot.filename = 'solid_plot'\n",
|
|
"plot.color_by = 'cell'\n",
|
|
"plot.opaque_domains = [cell1, cell2]\n",
|
|
"\n",
|
|
"model.plots = [plot]\n",
|
|
"model.plot_geometry(output=False)\n",
|
|
"Image('solid_plot.png')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"id": "9d85843e-803b-4645-96f9-cb7cd77f3080",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<bound method Model.differentiate_depletable_mats of <openmc.model.model.Model object at 0x7f4a7f34e990>>"
|
|
]
|
|
},
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "6416faa5-13f7-46e1-9135-f6ad88f50afc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|